[R-sig-ME] Best way to handle missing data?
David.Duffy at qimr.edu.au
Mon Mar 2 02:29:20 CET 2015
On Mon, 2 Mar 2015, Bonnie Dixon wrote:
> I don't think the model I am working on is a good candidate for structural
> equation modeling because the data set is very unbalanced (ie. there are
> very different numbers of observations for different people, taken at
> different times), the main relationship of interest involves a time-varying
> predictor, and one of the variables with missing data is not continuous (it
> is a binary, categorical variable). So, I will stick with the multiple
> imputation approach for handling the missing data.
As Wolfgang mentioned, OpenMX can fit a FIML analysis to irregular data.
If you were, for example, interested in a profile likelihood around a
variance component, that might be the way to go. It seems to me that
multiple imputation might not always respect complicated
clustering/correlation, depending on the actual method. A quick search
found some cautionary tales in:
Just another 2c, David.
| David Duffy (MBBS PhD)
| email: David.Duffy at qimrberghofer.edu.au ph: INT+61+7+3362-0217 fax: -0101
| Genetic Epidemiology, QIMR Berghofer Institute of Medical Research
| 300 Herston Rd, Brisbane, Queensland 4006, Australia GPG 4D0B994A
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